U.S. patent application number 11/704451 was filed with the patent office on 2008-08-14 for devices and methods for monitoring physiological information relating to sleep with an implantable device.
Invention is credited to Barbara Gibb, Benjamin D. Pless, Felice Sun.
Application Number | 20080195166 11/704451 |
Document ID | / |
Family ID | 39686526 |
Filed Date | 2008-08-14 |
United States Patent
Application |
20080195166 |
Kind Code |
A1 |
Sun; Felice ; et
al. |
August 14, 2008 |
Devices and methods for monitoring physiological information
relating to sleep with an implantable device
Abstract
Described here are implantable devices and methods for
monitoring physiological information relating to sleep. The
implantable devices are generally designed to include at least one
sensor for sensing physiological information, a processor for
processing the physiological information using low computational
power to detect a sleep stage, and a battery. The detected sleep
stage information may then be used to indicate sleep quality,
identify or monitor a medical condition, or guide treatment
thereof.
Inventors: |
Sun; Felice; (Palo Alto,
CA) ; Pless; Benjamin D.; (Atherton, CA) ;
Gibb; Barbara; (Foster City, CA) |
Correspondence
Address: |
MORRISON & FOERSTER LLP
755 PAGE MILL RD
PALO ALTO
CA
94304-1018
US
|
Family ID: |
39686526 |
Appl. No.: |
11/704451 |
Filed: |
February 9, 2007 |
Current U.S.
Class: |
607/18 ;
607/17 |
Current CPC
Class: |
A61B 5/4815 20130101;
A61B 5/14551 20130101; A61B 5/1118 20130101; A61B 5/02438 20130101;
A61B 5/369 20210101; A61B 5/0205 20130101; A61B 5/291 20210101;
A61B 5/349 20210101; A61B 5/11 20130101; A61B 5/01 20130101; A61B
5/026 20130101; A61N 1/36135 20130101; A61B 5/4812 20130101; A61B
5/316 20210101; A61B 5/686 20130101; A61N 1/36082 20130101 |
Class at
Publication: |
607/18 ;
607/17 |
International
Class: |
A61N 1/08 20060101
A61N001/08 |
Claims
1. An implantable device for monitoring physiological information
relating to sleep comprising: at least one sensor configured to
sense physiological information; and a processor configured to
process the physiological information using low computational power
to detect at least one sleep state or stage; and a battery.
2. The device of claim 1 further comprising memory configured to
store at least a portion of the physiological information or the
processed physiological information.
3. The device of claim 1 further comprising a wireless system
configured to transmit at least a portion of the physiological
information or the processed physiological information to a remote
location.
4. The device of claim 1 wherein the physiological information
comprises electrographic information.
5. The device of claim 4 wherein the electrographic information is
obtained from an electroencephalogram (EEG) recording.
6. The device of claim 4 wherein the electrographic information is
obtained from an electrocorticogram (ECOG) recording.
7. The device of claim 4 wherein the electrographic information is
obtained from an electrocardiogram (ECG) recording.
8. The device of claim 4 wherein the electrographic information
comprises cardiac information.
9. The device of claim 1 further comprising at least one
stimulation electrode.
10. The device of claim 1 wherein the at least one sensor is an
electrode that is also configured to provide stimulation.
11. The device of claim 1 further comprising a drug pump.
12. The device of claim 1 further comprising at least two sensors
configured to sense physiological information.
13. The device of claim 12 wherein the at least two sensors sense
the same type of physiological information.
14. The device of claim 12 wherein the at least two sensors sense a
different type of physiological information.
15. The device of claim 1 wherein the battery is rechargeable.
16. The device of claim 1 further comprising a component for
recording events configured to be triggered by a patient.
17. The device of claim 16 wherein the patient-triggered
event-recording component is a sensor configured to sense a change
in magnetic field.
18. The device of claim 1 further comprising a positional sensor
configured to sense a position of a user.
19. The device of claim 6 wherein at least one sensor is configured
to sense physiological information selected from the group
consisting of: blood oxygen saturation, partial pressure of oxygen
within blood, core temperature, head movement, cerebral metabolic
rate, and cerebral blood flow.
20. The device of claim 1 wherein the processor further processes
the physiological information to indicate the quality of sleep.
21. The device of claim 1 wherein the sensor is either a depth
electrode or a cortical electrode and is configured for monopolar
or bipolar sensing.
22. A method for monitoring physiological information relating to
sleep comprising sensing physiological information and processing
the physiological information using low computational power to
detect at least one sleep state or stage.
23. The method of claim 22 further comprising storing at least a
portion of the physiological information or the processed
physiological information.
24. The method of claim 22 further comprising transmitting at least
a portion of the physiological information or the processed
physiological information to a remote location.
25. The method of claim 22 further comprising treating a
condition.
26. The method of claim 25 wherein the condition is selected from
the group consisting of a neurological condition, a psychological
condition, a cardiac condition, a respiratory condition, and a
sleep condition.
27. The method of claim 25 wherein the condition is a movement
disorder.
28. The method of claim 27 wherein the movement disorder is
selected from the group consisting of Parkinson's disease,
Tourette's disorder, tremor, and restless leg syndrome.
29. The method of claim 27 wherein the condition is epilepsy.
30. The method of claim 27 wherein the condition is depression.
31. The method of claim 27 wherein the condition is a sleep
condition.
32. The method of claim 25 wherein treating the condition comprises
stimulation.
33. The method of claim 32 wherein the stimulation is selected
based on the detection of the at least one sleep stage.
34. The method of claim 32 wherein the stimulation can be regulated
based on the detection of the at least one sleep stage.
35. The method of claim 34 wherein the stimulation is regulated by
changing the frequency, pulse-width, amplitude, duration, or the
stimulation montage.
36. The method of claim 32 wherein the stimulation can be initiated
or terminated based on the detection of the at least one sleep
stage.
37. The method of claim 25 wherein treating the condition comprises
drug delivery.
38. The method of claim 37 wherein the drug delivery is selected
based on the detection of the at least one sleep stage.
39. The method of claim 37 wherein the drug delivery can be
regulated based on the detection of the at least one sleep
stage.
40. The method of claim 39 wherein the drug delivery is regulated
by changing the dosage, drug delivery rate, or drug selection.
41. The method of claim 37 wherein the drug delivery can be
initiated or terminated based on the detection of the at least one
sleep stage.
42. The method of claim 22 wherein processing the physiological
information utilizes an algorithm that uses low computational
power.
43. The method of claim 42 wherein the algorithm uses half
waves.
44. The method of claim 42 wherein the algorithm uses a histogram
counter.
45. The method of claim 42 wherein the algorithm uses a threshold
detector.
46. The method of claim 42 wherein the algorithm uses Fourier
transforms.
Description
FIELD
[0001] The implantable devices and methods described here relate to
the field of sleep staging and sleep monitoring. More specifically,
the implantable devices and methods relate to sleep staging and
sleep monitoring that is accomplished by detecting and analyzing
physiological signals.
BACKGROUND
[0002] Sleep is a state of brain activity defined as
unconsciousness from which a person can be aroused by sensory or
other stimuli (Arthur C. Guyton, Textbook of Medical Physiology 659
(8.sup.th ed. 1991)). While asleep, a person typically goes through
two alternating states of sleep, rapid eye movement (REM) sleep and
non-REM sleep. Non-REM sleep is comprised of four sleep stages.
Stage 1 (S1) is a state of drowsiness or transition between wake
and sleep. Stage 2 (S2) is a state of light sleep. Stage 3 (S3) and
stage 4 (S4) are stages of deep sleep. REM sleep occurs about 80 to
100 minutes after falling asleep, and is characterized by high
frequency EEG activity, bursts of rapid eye movement, and
heightened autonomic activity. Sleep progresses in a cycle from
stage 1 through stage 4 to REM sleep. A person typically
experiences four to six REM periods per sleep period.
[0003] Sleep disorders such as sleep apnea, restless legs syndrome,
and narcolepsy, inevitably result in sleep deprivation, which among
other things, interferes with work, driving, and social activities.
Various neurological and psychiatric conditions are also associated
with disruptions of normal sleep patterns. For example, insomnia or
oversleeping may occur in individuals with depression, and those
experiencing a manic upswing characteristic of bipolar disorder may
not sleep at all. Furthermore, because REM sleep increases
sympathetic nervous system activity, myocardial ischemia or
arrhythmia may be triggered during REM sleep in individuals with
preexisting heart pathology. Thus, the detection and
characterization of sleep states is important in the evaluation and
treatment of many medical conditions.
[0004] Currently, information about sleep stages is obtained using
polysomnography, a procedure in which data is acquired related to
various body activities, including EEG waveforms, cardiac muscle
activity, and breathing. This type of evaluation usually is
conducted in a sleep laboratory over one or more nights, and thus
is not convenient when it is desirable to monitor a patient over an
extended period of time. There are ambulatory sleep monitoring
systems, however continuous and extended sleep monitoring can
nonetheless be inconvenient because these systems must be applied
to the patient and adjusted prior to every sleep episode.
Furthermore, many known devices and systems for assessing EEG
waveforms (e.g., using time-domain and frequency-domain analysis)
require more computational ability than can be easily included in
an implantable device.
[0005] Accordingly, it would be desirable to have an implantable
device capable of continuously monitoring sleep. It would also be
desirable to have implantable devices that are capable of detecting
different stages of sleep. Similarly, it would be desirable to have
implantable devices with monitoring and detecting functions for
evaluating and treating various medical conditions associated with
disturbed sleep patterns.
SUMMARY
[0006] Described here are implantable devices and methods for
monitoring sleep and detecting various sleep stages. To detect at
least one sleep stage, the implantable devices generally include at
least one sensor designed to sense physiological information, a
processor configured to process the physiological information using
low computational power, and a battery that may or may not be
rechargeable. The implantable devices are also capable of
processing the physiological information to indicate the quality of
sleep, and characterize the information in a manner that helps to
diagnose various medical conditions and/or to guide therapy. In
some variations, the implantable device is configured to deliver
one or more therapies. For example, the implantable device may
contain a drug pump or drug-eluting electrode, or may be configured
to deliver electrical stimulation.
[0007] The implantable device may also include memory that is
configured to store at least a portion of the physiological
information or the processed physiological information.
Additionally, the implantable device may employ a wireless system
for transmitting at least a portion of the physiological
information or processed physiological information to a remote
location.
[0008] The type of sensor(s) that are included in the implantable
device will depend upon the type of physiological information that
it is desired to sense and process. For example, cardiac sensors
may be used to obtain cardiac information; neurological sensors may
be used to obtain neurological information, positional sensors may
used to obtain positional information, and respiratory sensors may
be used to obtain respiratory information. More specifically, the
types of physiological information that may be sensed include, but
are not limited to, heart rate, blood-oxygen saturation, partial
pressure of oxygen within the blood, core temperature, head
movement, cerebral metabolic rate, and cerebral blood flow. In some
instances, the physiological information obtained is electrographic
in form. For example, the physiological information may be recorded
as an electroencephalogram (EEG), electrocorticogram (ECoG),
electrocardiogram (EKG), electromyogram (EMG), or electro-oculogram
(EOG).
[0009] In one variation, the implantable device may contain at
least one sensor, and in other variations, it may include two or
more sensors. When two or more sensors are present, they may sense
the same or different types of physiological information. The
sensor may be either a depth electrode or strip electrode,
depending on the nature of the physiological information to be
sensed. Examples of strip electrodes include, but are not limited
to, electrodes that are placed within or beneath the scalp or on
the skull, or electrodes that are epidurally or subdurally placed.
Similarly, the depth or strip electrodes may employ monopolar or
bipolar sensing. In some variations, the implantable device further
includes a stimulation electrode or at least one sensor that is
configured to provide stimulation as well as sensing. In other
variations, the implantable device further includes a component
that is capable of patient-triggered event recording. This
component may be a sensor that is designed to sense magnetic field
changes.
[0010] Methods for detecting sleep stages are also described. In
one variation, the method includes sensing physiological
information and processing that information using low computational
power to detect at least one sleep stage. The REM state, the stages
S1 S2, S3, S4, or any combination of the REM state and the sleep
stages may be detected. The algorithm may employ half-waves, a
histogram counter, a threshold detector, Fourier transforms, or a
combination thereof to process the physiological information using
low computational power.
[0011] The information obtained related to sleep states and stages
may be used to diagnose or treat medical condition, or to guide
therapy for a medical condition. Examples of medical conditions
include neurological conditions, psychological conditions, cardiac
conditions, respiratory conditions, and sleep conditions. In one
variation, stimulation is used to treat a medical condition. The
stimulation may be selected and regulated based on the sleep state
or stage that is detected. Specifically, the stimulation may be
regulated by changing the frequency, pulse-width, amplitude, or
duration of the stimulation, or by changing the stimulation
montage. The stimulation may also be initiated or terminated based
on the detection of a particular sleep state or stage.
[0012] In another variation, the medical condition is treated by
delivery of a drug. The type of drug delivered, dosage, and/or rate
of delivery may be based on the particular sleep state or stage
detected, Similarly, when drug delivery is initiated or terminated
or how drug delivery is regulated may be determined based upon the
sleep state or stage a person is experiencing.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a side view of a portion of a person's head where
locations are identified at which an implantable device for
monitoring physiological information relating to sleep may be
implanted.
[0014] FIG. 2 is a perspective view of an implantable device for
monitoring physiological information relating to sleep within the
cranium.
[0015] FIG. 3 is a schematic diagram of a method for monitoring
physiological informatin relating to sleep.
[0016] FIG. 4 is a block diagram of an implantable device for
monitoring phisilogical information relating to sleep.
[0017] FIG. 5 is a block diagram of a detection subsystem of the
implantable device shown in FIG. 4.
[0018] FIG. 6 is a block diagram of a sensing front end of the
detection subsystem of FIG. 5.
[0019] FIG. 7 is a block diagram of a waveform analyzer of the
detection subsystem of FIG. 5.
[0020] FIG. 8 is a block diagram of certain components of the
waveform analyzer of the detection subsystem of FIG. 5.
[0021] FIG. 9A is a graphical representation of an EEG signal
illustrating the signal decomposed into time windows and
samples.
[0022] FIGS. 9B-9D are graphical representations of EEG signals
that may be detected during stages of sleep. FIG. 9B is a graph of
a slow delta wave. FIG. 9C is a graph of a theta wave. FIG. 9D is a
graph of an alpha wave.
[0023] FIG. 10A is another graph of the EEG signal of FIG. 9A,
illustrating half waves extracted from the signal.
[0024] FIG. 10B is a graph of an ECoG signal, illustrating minimum
and maximum signal amplitudes and durations.
[0025] FIG. 11 is a flow chart of a method of extracting half waves
from an EEG signal.
[0026] FIG. 12 is a flow chart of a method of analyzing half waves
from an EEG signal
[0027] FIG. 13 is a flow chart of a method of applying an X of Y
criterion to half-wave windows using a central processing unit.
[0028] FIG. 14 is a graph of the EEG signal of FIG. 9A,
illustrating a line-length function calculation.
[0029] FIG. 15 is a flow chart of a method of calculating the
line-length function of FIG. 14 using the waveform analyzer of FIG.
7.
[0030] FIG. 16 is a flow chart of a method of calculating and
analyzing a line-length function of an EEG signal
[0031] FIG. 17 is a graph of the EEG signal of FIG. 9A,
illustrating an area function calculation.
[0032] FIG. 18 is a flow chart of a method FIG. 7 of calculating an
area function as illustrated in FIG. 17 using the waveform analyzer
or FIG. 7.
[0033] FIG. 19 is a flow chart of a method of calculating and
analyzing an area function of an EEG signal
[0034] FIG. 20 is a flow chart of a method of analyzing half wave,
line length, and area information using an event-driven feature of
the central processing unit.
[0035] FIG. 21 is a flow chart of a method in which analytic tools
are combined with a detection channel in an implantable device for
monitoring physiological information relating to sleep.
[0036] FIG. 22 is a flow chart of a method in which detection
channels are combined in an event detector.
[0037] FIG. 23 is a histogram of delta half-wave counts, where
x-axis variable is time and the y-axis variable is the delta
half-wave counts.
DETAILED DESCRIPTION
[0038] Described here are implantable devices and methods for
monitoring physiological information relating to sleep for
detecting sleep states or stages. The devices may be implanted in
or on or adjacent to suitable tissue of any body region. The
physiological information may be used to monitor the sleep of a
patient, and/or used to diagnose or guide therapy of a medical
condition.
[0039] The devices are configured to use low computational power to
perform device functions. For example, the devices may use
customized digital electronics modules that require low power and
are minimally reliant upon a central processing unit to implement
algorithms to reduce or extract features from data. One advantage
of using algorithms that require low computational power is that
the actual power consumed by the devices is less than what it would
be if a greater degree of computational power was required for the
device to carry out its intended functions. Preserving power in an
implantable device is especially desirable when the power source
for the device, such as a battery, is implanted as well. That is,
it is generally desirable for the power supply to be sufficient to
carry out the device functions for as long a period of time as
possible, to avoid removing or explanting the device to replace it
or recharge it. Removing an implantable device can also cause
damage to the device leads or other functional or structural
features of the device and, of course, explanting or removing any
implantable device poses the risk of complications from the surgery
which it would be desirable to avoid whenever possible.
[0040] Devices for Detecting Sleep Stages
[0041] The implantable devices for monitoring physiological
information relating to sleep are configured to identify any sleep
state, i.e., REM or non-REM, and any of the four standard sleep
stages S1, S2, S3, and S4, or any combination of sleep states and
stages. In one variation, the implantable device includes at least
one sensor that senses physiological information, a processor that
processes the physiological information using low computational
power to detect at least one sleep state or stage, and a battery.
The sensor(s) may be included in a detection subsystem that may
also contain a waveform analyzer. The waveform analyzer may be
capable of analyzing waveform features (such as half wave
characteristics) as well as window-based analyses (such as line
length and area under the curve) to provide sleep [state or?] stage
detection. A central processing unit is typically included to
consolidate the data received from one or more sensors and
coordinate action in reaction or response to the data received when
necessary.
[0042] One variation of an implantable device is illustrated in
FIG. 2. In FIG.2, the implantable device 110 is shown within one of
the parietal bones 210 of the skull of a patient, in a location
anterior to the lambdoidal suture 212. It should be noted, however,
that the placement described and illustrated herein is merely
exemplary, and other locations and configurations are also
possible, in the cranium or elsewhere, depending on the size and
shape of the device and individual patient needs, among other
factors. The device 110 may be configured to fit the contours of
the patient's cranium 214. In another variation (shown in FIG. 1),
the device may be implanted within or under the scalp 100, within
or under the skull 102, under the dura mater 104, or within the
brain 106. In yet another variation, the device may be implanted
within the chest wall, e.g., within the pectoral region (not
shown), with leads extending through the patient's neck and between
the patient's cranium and scalp, as necessary.
[0043] Referring to FIG. 2, the implantable device 110 may be
placed within the patient's cranium 214 by way of a ferrule 216.
The ferrule 216 is a structural member adapted to fit into a
cranial opening, attach to the cranium 214, and retain the device
110. To implant the device 110, a craniotomy is performed in the
parietal bone anterior to the lambdoidal suture 212 to define an
opening 218 slightly larger than the device 110. The ferrule 216 is
inserted into the opening 218. and affixed to the cranium 214, as
with bone screws ensuring a tight and secure fit. The device 110 is
then inserted into and affixed to the ferrule 216.
[0044] As shown in FIG. 2, the device 110 includes a lead connector
220 adapted to receive one or more electrical leads, such as a
first lead 222. The lead connector 220 acts to physically secure
the lead 222 to the device 110, and facilitates the electrical
connection between a conductor in the lead 222 and circuitry within
the device 110. The lead connector 220 accomplishes this in a
substantially fluid-tight environment with biocompatible
materials.
[0045] The lead 222 is a flexible elongated member having one or
more conductors. The lead 222 is coupled to the device 110 via the
lead connector 220, and is generally situated on the outer surface
of the cranium 214 (and under the patient's scalp 100), extending
between the device 110 and a burr hole 224 or other cranial
opening, where the lead 222 enters the cranium 214 and is coupled
to an electrode implanted in a desired location in the patient's
brain. As described in U.S. Pat. No. 6,006,124 to Fischell, et al.,
which is hereby incorporated by reference in its entirety, the burr
hole 224 may be wholly or partially sealed, e.g., with a burr hole
cover, after implantation to prevent further movement of the lead
222.
[0046] The device 110 may include a durable outer housing 226
fabricated from a biocompatible material. Titanium, which is light,
extremely strong, and biocompatible, is used in analogous devices,
such as cardiac pacemakers, and would serve advantageously in this
context. As the device 110 is self-contained, the housing 226
encloses a battery and any electronic circuitry necessary or
desirable to provide the functionality described herein, as well as
any other desirable features. As will be described in further
detail below, a telemetry coil may be provided outside of the
housing 226 (and potentially integrated with the lead connector
220) to facilitate communication between the device 110 and
external devices. The battery included in the implantable devices
may be rechargeable. In general, the implantable devices may
generally have a long-term average current consumption on the order
of 10 microamps, which allows them to operate on power provided by
a coin cell or similarly small battery for a period of years
without need for battery replacement. It should be noted, however,
that as battery and power supply configurations vary, the long-term
average current consumption of a device may also vary and still
provide satisfactory performance.
[0047] The configuration of the implantable device 110 provides
several advantages over known alternative designs. First, the
self-contained nature of the device substantially decreases the
need for access to the device 110, allowing the patient to
participate in normal life activities. Its small size and
intracranial placement also may cause minimal cosmetic
disfigurement. The device 110 will typically fit in an opening in
the patient's cranium, under the patient's scalp, with little
noticeable protrusion or bulge. Furthermore, the ferrule 216 used
for implantation allows a craniotomy to be performed and fit
verified before the device 110 is implanted, thus avoiding the risk
of breaking the device during this fitting process. The ferrule 216
also protects the brain from the device in the event the brain is
subjected to significant external pressure or impact by providing a
structural support for the device in the skull. A further advantage
is that the ferrule 216 receives any cranial bone growth, so when
the device is explanted or removed, any cranial bone that has grown
since the time of implant will impinge upon the ferrule, not the
device, so that the device can be taken out and/or replaced with
another device without the need to remove any bone screws that are
used to secure the ferrule 216. Only the fasteners that are used to
retain the device 110 in the ferrule 216 need be manipulated.
[0048] As stated above, and as illustrated in FIG. 3, the
implantable device may operate in conjunction with external
equipment. The device 110 is mostly autonomous (particularly when
performing its usual sensing, detection, and stimulation
capabilities), but may include a wireless link 310 that can be
selectively engaged to external equipment such as a programmer 312
(e.g., a handheld programmer). The wireless link 310 may be
established by moving a wand or other suitable apparatus into range
of the device 110 where the wand or other apparatus has wireless
communication capabilities and is coupled to the programmer 312.
The programmer 312 can then be used to manually control the
operation of the device 110, as well as to transmit information to,
or receive information from, the device 110. Several specific
capabilities and operations the programmer 312 can perform in
conjunction with the device 110 are described in further detail
below.
[0049] The programmer 312 is capable of performing a number of
operations. In particular, the programmer 312 may be able to
specify and set variable parameters in the device 110 to adapt the
function of the device 110 to meet the patient's needs, download or
receive data (including but not limited to stored electrographic
waveforms, detections, parameters, or logs of actions taken) from
the device 110 to the programmer 312, upload or transmit program
code and other information from the programmer 312 to the device
110, or command the device 110 to perform specific actions or
change modes as desired by a physician operating the programmer
312. To facilitate these functions, the programmer 312 is adapted
to receive physician input 314 and provide physician output 316.
The data may be transmitted between the programmer 312 and the
device 110 over the wireless link 310.
[0050] The programmer 312 may be coupled via a communication link
318 to a network 320 such as the Internet. This allows any
information downloaded from the device 110, as well as any program
code or other information to be uploaded to the device 110, to be
stored in a database at one or more data repository locations
(which may include various servers and network-connected
programmers like the programmer 312). Patients and/or physicians
can thereby have access to important data, including past
monitoring results and/or information concerning treatment of
medical conditions and the results of treatment, and software
updates, essentially anywhere in the world where there is a
programmer (like the programmer 312) and a network connection.
[0051] A block diagram of an implantable device 110 for monitoring
physiological information relating to sleep is illustrated in FIG.
4. Inside the housing 226 (shown in FIG. 2) of the device 110 there
are several subsystems making up a control module 410. The control
module 410 is capable of being coupled to a plurality of electrodes
412, 414, 416, and 418 (each of which may be connected to the
control module 410 via a lead that is analogous or identical to the
lead 222 of FIG. 2) for sensing physiological information and
providing stimulation, when desired. The coupling may be
accomplished through the lead connector 220 (FIG. 2). Although four
electrodes are shown in FIG. 4, it should be recognized that any
number of electrodes is possible. For example, it is possible to
employ a single lead with at least two electrodes, or two leads
each with a single electrode (or with a second electrode provided
by a conductive exterior portion of the housing 226).
[0052] The electrodes 412, 414, 416, and 418 are connected to an
electrode interface 420. The electrode interface is capable of
selecting each electrode as required for sensing and/or
stimulation. Accordingly, the electrode interface 420 is coupled to
a detection subsystem 422 and/or a stimulation subsystem 424. When
the electrodes 412, 414, 416, and 418 are designed primarily for
sensing physiological information, a stimulation subsystem 424 will
typically not be included in the control module 410. The electrode
interface 420 also may provide any other features, capabilities, or
aspects, including but not limited to, amplification, isolation,
and charge-balancing functions, that are required for a proper
interface with, e.g., neurological tissue, and not provided by any
other subsystem of the device 110.
[0053] The detection subsystem 422 may include an EEG (and/or ECOG)
analyzer function. The EEG analyzer function may be adapted to
receive EEG signals from the electrodes 412, 414, 416, and 418
through the electrode interface 420, and process those EEG signals
to identify neurological activity indicative of various sleep
states or stages. One way to implement such EEG analysis
functionality is disclosed in detail in U.S. Pat. No. 6,016,449 to
Fischell et al., incorporated by reference above. In another
variation, the detection subsystem may include a set of sensors
built specifically for sensing low-frequency EEG and/or ECoG
signals associated with sleep. These sensors may be configured as a
set of half-wave detectors with allowable minimum and maximum
amplitude ranges and window durations to sense signals within the
delta (0.5-3.5 Hz), theta (4-7.5 Hz), and alpha (8-12 Hz) bands.
Depending on the particular indication of use, however, the sleep
sensors may be adjusted to sense other frequency bands. The
detections may be made using bipolar or monopolar recording. While
monopolar recording is desirable since sleep patterns are often
synchronous over large cortical regions, adjusting the recording
montage appropriately may be necessary to optimize bipolar
recordings for sleep monitoring. The detection subsystem may
optionally also contain further sensing and detection capabilities,
including but not limited to parameters derived from other
physiological information (such as electrophysiological parameters,
temperature, blood pressure, heart rate, position (including head
position), movement, etc.).
[0054] The detection subsystem may use the preexisting electrodes
that were placed for neurostimulation for sleep state or stage
detection, or additional electrodes may be placed for optimum
detection. For example, if noncortical leads were implanted for
neurostimulation, an additional lead may be placed over the
occipital cortex, or other desired cortex location, for sleep
monitoring. Cardiac sensors (cardiac electrodes) may also be used
to sense electrical activity (e.g., heart rate and conduction) of
the heart. Respiratory sensors may be included that are configured
to sense various respiratory parameters, such as respiratory rate,
tidal volume, and minute ventilation.
[0055] The sensors may be coupled to a low-speed, low-power central
processing unit that detects and processes the sensor data. For
example, the central processing unit may be configured to have a
processing speed of between about 100 Hz to about 1000 Hz. In some
cases, a processing speed of less than 100 Hz may be utilized. A
processor may be included to receive EEG and/or ECoG data from the
sensors. The processor may perform sleep state or stage detection
on a real-time basis, or may process previously acquired and stored
sensor data in a batch mode to retrospectively classify sleep
stages of one or more sleep periods. The central processing unit
may remain in a suspended "quiet" state characterized by relative
inactivity for a substantial percentage of the time, and then may
be periodically awakened by interruptions from the detection
subsystem to perform certain tasks related to the detection and
prediction schemes enabled by the device.
[0056] If included, the stimulation subsystem 424 is capable of
applying electrical stimulation to tissue through the electrodes
412, 414, 416, and 418. This can be accomplished in any of a number
of different ways. For example, it may be advantageous in some
circumstances to provide stimulation in the form of a substantially
continuous stream of pulses or, alternatively, on a scheduled
basis. In some instances, therapeutic stimulation may be provided
in response to specific sleep stages detected by the EEG analyzer
function of the detection subsystem 422. As illustrated in FIG. 4,
the stimulation subsystem 424 and the EEG analyzer function of the
detection subsystem b are in communication with each other. This
facilitates the ability of stimulation subsystem 424 to provide
responsive stimulation as well as an ability of the detection
subsystem 422 to blank the amplifiers while stimulation is being
performed to minimize stimulation artifacts. It is contemplated
that the parameters of the stimulation signal (e.g., frequency,
duration, waveform) provided by the stimulation subsystem 424 would
be specified by other subsystems in the control module 410, as will
be described in further detail below.
[0057] A memory subsystem 426 and a central processing unit (CPU)
428, which can take the form of a microcontroller, may also be
included in the control module 410. The memory subsystem is coupled
to the detection subsystem 422 (e.g., for receiving and storing
data representative of sensed EEG signals and evoked responses),
the stimulation subsystem 424 (e.g., for providing stimulation
waveform parameters to the stimulation subsystem), and the CPU 428,
which can control the operation of the memory subsystem 426. In
addition to the memory subsystem 426, the CPU 428 may also be
connected to the detection subsystem 422 and the stimulation
subsystem 424 for direct control of those subsystems.
[0058] Also provided in the control module 410, and coupled to the
memory subsystem 426 and the CPU 428, is a communication subsystem
430. The communication subsystem 430 enables communication between
the device 110 and the outside world, particularly the external
programmer 312 (FIG. 3). As set forth above, the communication
subsystem 430 may include a telemetry coil (which may be situated
outside of the housing 226), which enables transmission and
reception of signals, to or from an external apparatus via
inductive coupling. Alternatively, the communication subsystem 430
may use an antenna for an RF link or an audio transducer for an
audio link.
[0059] Further subsystems in the control module 410 are a power
supply 432 and a clock supply 434. The power supply 432 supplies
the voltages and currents necessary for each of the other
subsystems. The clock supply 434 supplies substantially all of the
other subsystems with any clock and timing signals necessary for
their operation.
[0060] It should be observed that while the memory subsystem 426 is
illustrated in FIG. 4 as a separate functional subsystem, the other
subsystems may also require various amounts of memory to perform
the functions described above and others. Furthermore, while the
control module 410 is shown as a single physical unit contained
within a single physical enclosure, namely the housing 226 (FIG.
2), it may comprise a plurality of spatially separate units, with
each performing a subset of the capabilities described above. Also,
it should be noted that the various functions and capabilities of
the subsystems described above may be performed by electronic
hardware, computer software (or firmware), or a combination
thereof. The division of work between the CPU 428 and the other
functional subsystems may also vary.
[0061] The detection subsystem is further detailed in FIG. 5. In
FIG. 5, inputs from the electrodes 412, 414, 416, and 418 (FIG. 4)
are depicted on the left, and connections to other subsystems are
shown on the right. Signals received from the electrodes 412, 414,
416, and 418 (as routed through the electrode interface 420) are
received in an electrode selector 510. The electrode selector 510
allows the device to select which electrodes (of the electrodes
412, 414, 416, 418) should be routed to which individual sensing
channels of the detection subsystem 422, based on commands received
through a control interface 518 from the memory subsystem 426 or
the CPU 428 (FIG. 4). In one variation, each sensing channel of the
detection subsystem 422 receives a monopolar or bipolar signal
representative of the difference in electrical potential between
two selectable electrodes. Accordingly, the electrode selector 510
provides signals corresponding to each pair of selected electrodes
(of the electrodes 412, 414, 416, 418) to a sensing front end 512,
which performs amplification, analog-to-digital conversion, and
multiplexing functions on the signals in the sensing channels. The
sensing front end will be described further below in connection
with FIG. 6. In another variation, each sensing channel of the
detection subsystem 422 may receive a monopolar or bipolar signal
representative of the difference in electrical potential between an
electrode and the housing of the implantable device (which may also
serve as an electrode).
[0062] A multiplexed input signal representative of all active
sensing channels is then fed from the sensing front end 512 to a
waveform analyzer 514. The waveform analyzer 514 may include a
special-purpose digital signal processor (DSP) or, in another
variation, may comprise a programmable general-purpose DSP.
Referring to FIG. 5, the waveform analyzer may have its own
scratchpad memory area 516 for local storage of data and program
variables when signal processing is being performed. In either
case, the signal processor performs suitable measurement and
detection methods described generally above and in greater detail
below. Any results from such methods, as well as any digitized
signals intended for storage transmission to external equipment,
are passed to various other subsystems of the control module 410,
including the memory subsystem 426 and the CPU 428 (FIG. 4),
through a data interface 520. Similarly, the control interface 518
allows the waveform analyzer 514 and the electrode selector 510 to
be in communication with the CPU 428.
[0063] Referring now to FIG. 6, the sensing front end 512 is
illustrated in further detail. As shown, the sensing front end
includes a plurality of differential amplifier channels 610, each
of which receives a selected pair of inputs from the electrode
selector 510 or from the electrode selector 510 and implantable
device housing. In one variation, each of the differential
amplifier channels 610 is adapted to receive or to share inputs
with one or more other differential amplifier channels 610 without
adversely affecting the sensing and detection capabilities of the
implantable device. In another variation, there are at least eight
electrodes, which can be mapped separately to eight differential
amplifier channels 610 representing eight different sensing
channels and capable of individually processing eight bipolar
signals, each of which represents an electrical potential
difference between two monopolar input signals received from the
electrodes and applied to the sensing channels via the electrode
selector 510. For clarity, only five channels are illustrated in
FIG. 6, but it should be noted that any practical number of sensing
channels may be employed.
[0064] Each differential amplifier channel 610 feeds a
corresponding analog-to-digital converter (ADC) 612. The
analog-to-digital converters 612 may be separately programmable
with respect to sample rates. In one variation, the ADCs 612
convert analog signals into 10-bit unsigned integer digital data
streams at a sample rate selectable between 250 Hz and 500 Hz. In
other variations where waveforms are used, as described below,
sample rates of 250 Hz are typically used. However, numerous sample
rate and resolution options are possible, with tradeoffs known to
individuals of ordinary skill in the art of electronic signal
processing. The resulting digital signals are received by a
multiplexer 614 that creates a single interleaved digital data
stream representative of the data from all active sensing channels.
As will be described in further detail below, not all of the
sensing channels need to be used at one time, and it may in fact be
advantageous in certain circumstances to deactivate certain sensing
channels to reduce power consumption.
[0065] It should be noted that as illustrated and described herein,
a "sensing channel" is not necessarily a single physical or
functional item that can be identified in any illustration. Rather,
a sensing channel is formed from the functional sequence of
operations described herein, and particularly represents a single
electrical signal received from any pair or combination of
electrodes (including the implantable device housing, which can
serve as an electrode), as preprocessed by a method for monitoring
physiological information relating to sleep with an implantable
device, in both analog and digital forms. See, e.g., U.S. Pat. No.
6,473,639 to D. Fischell et al., filed on Mar. 2, 2000, and
entitled "Neurological Event Detection Using Processed Display
Channel Based Algorithms and Devices Incorporating These
Procedures," which is hereby incorporated by reference in its
entirety. At times (particularly after the multiplexer 614),
multiple sensing channels are processed by the same physical and
functional components of the system; notwithstanding that, it
should be recognized that unless the description herein indicates
to the contrary, the implantable devices described herein process,
handle, and treat each sensing channel independently. Referring
again to FIG. 6, the interleaved digital data stream is passed from
the multiplexer 614, out of the sensing front end 512, and into the
waveform analyzer 514. The waveform analyzer 514 is illustrated in
greater detail in FIG. 7.
[0066] In FIG. 7, the interleaved digital data stream representing
information from all of the active sensing channels is first
received by a channel controller 710. The channel controller
applies information from the active sensing channels to a number of
wave morphology analysis units 712 and window analysis units 714.
It is preferred to have as many wave morphology analysis units 712
and window analysis units 714 as possible, consistent with the
goals of efficiency, size, and low power consumption necessary for
an implantable device. In one variation, there are sixteen wave
morphology analysis units 712 and eight window analysis units 714,
each of which can receive data from any of the sensing channels of
the sensing front end 512, and each of which can be operated with
different and independent parameters, including differing sample
rates, as will be discussed in further detail below.
[0067] Each of the wave morphology analysis units 712 operates to
extract certain feature information from an input waveform as
described below in conjunction with FIGS. 9A-11. Similarly, each of
the window analysis units 714 performs certain data reduction and
signal analysis within time windows in the manner described in
conjunction with FIG. 12-17. Output data from the various wave
morphology analysis units 712 and window analysis units 714 may be
combined via event detector logic 716. The event detector logic 716
and the channel controller 710 may be controlled by control
commands 718 received from the control interface 518 (FIG. 5).
[0068] The term "detection channel," as used herein, refers to a
data stream including the active sensing front end 512 and the
analysis units of the waveform analyzer 514 processing that data
stream, in both analog and digital forms. It should be noted that
each detection channel can receive data from a single sensing
channel and each sensing channel can be applied to the input of any
combination of detection channels. The latter selection is
accomplished by the channel controller 710. As with the sensing
channels, not all detection channels need to be active; certain
detection channels can be deactivated to save power or if
additional detection processing is deemed unnecessary in certain
applications.
[0069] In conjunction with the operation of the wave morphology
analysis units 712 and the window analysis units 714, a memory area
(e.g., scratchpad memory) 516 may be provided for temporary storage
of processed data. The memory area 516 may be physically part of
the memory subsystem 426, or alternatively may be provided for the
exclusive use of the waveform analyzer 514 Other subsystems and
components of the implantable device may also be furnished with
local memory if such configurations are beneficial.
[0070] The operation of the event detector logic 716 (i.e., sleep
stage detector logic) is illustrated in detail in the functional
block diagram of FIG. 8, which shows four exemplary sensing
channels 810, 814, 818, and 820 and three illustrative event
detectors 812, 816, and 822 for analyzing the data from the sensing
channels. A first sensing channel 810 ("Channel 1" in FIG. 8)
provides input to a first event detector 812. (It should be
recognized the blocks shown in FIG. 8 denote function, and a
particular block may or may not correspond to a physical structure
in the implantable device.) A second sensing channel 814 ("Channel
2" in FIG. 8) provides input to a second event detector 816, and a
third sensing channel 818 ("Channel 3" in FIG. 8) and a fourth
sensing channel 820 ("Channel 4" in FIG. 8) both provide input to a
third event detector 822.
[0071] The processing that is performed in association with each of
the three event detectors 812, 816, and 822 will now be described,
again with reference to FIG. 8. The first sensing channel 810
("Channel 1") feeds a signal to the first event detector 812, which
is applied to both a wave morphology analysis unit 824 (e.g., one
of the wave morphology analysis units 712 of FIG. 7) and a window
analysis unit 826 (e.g., one of the window analysis units 714 of
FIG. 7). The window analysis unit 826, in turn, includes a line
length analysis tool 828 and an area analysis tool 830. As will be
discussed in detail below, the line length analysis tool 828 and
the area analysis tool 830 analyze different aspects of the signal
from the first input channel 810.
[0072] The outputs from the wave morphology analysis unit 824, the
line length analysis tool 828, and the area analysis tool 830 of
the first event detector 812 are combined in a Boolean "AND"
operation 832 into a single output for the first event detector
834. The output 834 then may be used by another system or subsystem
for further monitoring or analysis. For example, if a combination
of analysis tools in an event detector identifies several
simultaneous (or near-simultaneous) types of activity in an input
channel, another system or subsystem in the implantable device or
operating in conjunction with the implantable device may be
programmed to perform an action in response to the output 834.
Details of the analysis tools and the combination processes used in
the event detectors herein described will be set forth in greater
detail below.
[0073] In the second event detector 816, which receives input from
second sensing channel 814 ("Channel 2"), only a wave morphology
analysis unit 836 is active. Accordingly, no Boolean operation
needs to be performed, and the wave morphology analysis unit 836 is
used directly as an event detector output 838.
[0074] The third event detector 822 receives signals from both the
third input sensing channel 818 ("Channel 3") and the fourth input
sensing channel 820 ("Channel 4"), and includes two separate
detection channels of analysis units: a first wave morphology
analysis unit 840 and a first window analysis unit 842, the first
window analysis unit 842 including a first line length analysis
tool 844 and a first area analysis tool 846; and a second wave
morphology analysis unit 848 and a second window analysis unit 850,
the second window analysis unit 850 including a second line length
analysis tool 852 and a second area analysis tool 854. The two
detection channels of analysis units in the third event detector
822 are thus processed and operated on so as to provide a single
third event detector output 856.
[0075] More specifically, the signal from the third sensing channel
818 is input into one of the two wave morphology analysis units
(e.g., wave morphology analysis unit 840 in FIG. 8) of the third
event detector 822, and the signal from the fourth sensing channel
820 is input into the other of the two wave morphology analysis
units (e.g., wave morphology analysis unit 848 in FIG. 8). Each of
the inputs from the third sensing channel 818 and the fourth
sensing channel 820 is also introduced into a window analysis unit
(e.g., the signal from the third sensing channel 818 is input into
window analysis unit 842 in FIG. 8 as well as into wave morphology
analysis unit 840, and the signal from the fourth sensing channel
820 is input into window analysis unit 850 as well as into wave
morphology unit 848. Each of the two window analysis units 842 and
850 of the third event detector 822 have a line length analysis
tool (844 and 852) and an area analysis tool (846 and 854). Thus,
there are three analysis unit outputs associated with one of the
two channels in the third event detector 822 (e.g., third sensing
channel 818) and the same three types of analysis unit outputs
associated with the other of the two channels in the third event
detector 822 (e.g., fourth sensing channel 820). The three analysis
unit outputs associated with each channel 818, 820 can be combined
via a Boolean "AND" operation 858, 862 to produce two different
outputs of a first stage of the third event detector, 860 and 864,
respectively. These two outputs 860 and 864 can be combined in a
second Boolean "AND" operation 868 into a second stage to produce
the final output for the third event detector 856.
[0076] Depending upon the nature of the signals being sensed and
any processing that is applied to the sensed signals in the event
detectors, it may be desirable to invert one or more outputs before
or after the outputs are combined in a Boolean "AND" operation. For
example, with reference to the third event detector 822, the one of
the two outputs of the first stage of the third event detector,
i.e., the output 864 in FIG. 8 is shown being inverted in FIG. 8
(as represented in the figure by the "NOT" gate or inverter symbol
866), before the output 864 is further combined in a Boolean "AND"
operation with the other output of the first stage of the third
event detector, i.e., output 860 in FIG. 8, to produce a
second-stage third event detector output 856.
[0077] In one variation of an event detector such as the third
event detector 822 shown in FIG. 8, the event detector can be
configured to monitor or detect a "qualifying event," such as the
occurrence of the REM sleep state. More particularly, the
processing of, and logical operations applied to, the data input to
the third sensing channel 818 and the fourth sensing channel 820
may be such that the second-stage output of the third event
detector, i.e., output 856, only is "on" (or "high" or a "1" value
as opposed to a "0" value) when a patient is in the REM sleep
state. For example, the input received by the third sensing channel
818 may correspond to an EEG signal from the left hemisphere of a
patient's brain, and the input received by the fourth sensing
channel 820 may correspond to an EEG signal from the right
hemisphere of the patient's brain. When both sensing channels 818
and 820 are sensing an EEG signal with characteristics indicative
of the REM sleep state, the third event detector 822 can be
configured so that the output of the second stage of the third
event detector (output 856) will be on (or "high" or a "1" value)
for so long as the two EEG signals are indicative of REM sleep. In
addition, the both first stage outputs of the third event detector
(860 and 864) can be independently configured for selectable
persistence (i.e., the ability to retain a value indicating the
presence of REM sleep some time after the REM sleep state is
actually sensed by the third sensing channel 818 and the fourth
sensing channel 820. Thus, the system can be configured so that the
combination of the two outputs of the first-stage of the third
event detector, namely, outputs 860 and 864, specifically the
second-stage output of the third event detector 856 (shown in FIG.
8 as the result of the Boolean "AND" operation illustrated by "AND"
gate 868), will remain triggered even when there is not precise
temporal synchronization between detections on the sensing
channels.
[0078] The EEG signals may be introduced to the sensing channels by
amplifying signals detected by electrodes capable of sensing
electrical activity in the brain. It will be apparent that the
location of the electrodes may be other than in the right and left
hemispheres, and that the signals received by the sensing channels
may be other than EEG signals. Moreover, it will be apparent that
the same use of the event detectors of the implantable device, such
as the third event detector 822 of FIG. 8, can be used to configure
one or more "qualifying channels" for different types of signals,
and that the output that represents that a qualifying event is
occurring or has occurred may reflect use of something other than a
Boolean "AND" operation (e.g., a Boolean "OR" operation) to
identify that the event is occurring or has occurred.
[0079] In one variation, the "qualifying channel" is applied to
detect when noise is occurring on a channel in excess of a certain
threshold, so that the output of the second stage of the third
event detector output 856 is only "on" when the noise is below that
threshold. In another variation, the "qualifying channel" is
applied to aid in configuring the implantable device 110, for
example, to help determine which of two sets of detection
parameters is preferable (by configuring two sensing channels of an
event detector to receive signals each corresponding to a different
set of parameters on each of two sensing channels of an event
detector, and then using a Boolean "OR" operation to indicate when
which set of parameters is present on which channel. In still
another variation, the sensing channels, processing of and logical
operations on the signals can be configured so that a specific
temporal sequence of detections must be occurring or have occurred
in order for the ultimate output of the event detector to be "on."
There are numerous other possibilities for signal processing and
logical operations for the event detectors of the implantable
device 110. For example, a first event detector output 834, a
second event detector output 838 and a third event detector output
856 may be represented by Boolean flags and, as described below,
provide information useful for the operation of the implantable
devices 110.
[0080] While FIG. 8 illustrates four different sensing channels
providing input to three event detectors, it should be noted that
in a maximally flexible variation, each sensing channel would be
allowed to connect to one or more event detectors. It may be
advantageous to program (using hardware, firmware, software, or
some combination thereof) the different event detectors with
different settings (e.g., thresholds) to facilitate alternate
"views" of the same sensing channel data stream.
[0081] FIG. 9A shows three different graphs of a waveform
(amplitude vs. time) of the type that may be detected by the
implantable devices 110 described herein. In the top graph of FIG.
9A, the waveform 910 is representative of an unprocessed EEG or
ECoG waveform having a substantial amount of variability; the
illustrated segment has a duration of approximately 160 ms and a
dominant frequency (visible as the large-scale crests and valleys)
of approximately 12.5 Hz. It will be recognized that the first
waveform is rather rough and characterized by many peaks of small
amplitude and duration; there is a substantial amount of
high-frequency energy represented therein.
[0082] The middle graph of FIG. 9A shows the waveform of the top
graph after it has been filtered to remove most of the
high-frequency energy In the middle graph, the waveform 912 is
significantly smoother than the waveform 910 shown in the top
graph. The filtering operation may be performed in the sensing
front end 512 before the analog-to-digital converters 612 (FIG. 6).
The filtered waveform 912 then can be sampled by one of the
analog-to-digital converters 612. The result of such a sampling
operation is represented graphically in the bottom waveform 914
shown in FIG. 9A. As illustrated, a sample rate used in one
variation is 250 Hz (4.0 ms sample duration), resulting in
approximately 40 samples over the illustrated 160 ms segment. As is
well known in the art of digital signal processing, the amplitude
resolution of each sample is limited. In FIG. 9A, each sample was
measured with a resolution of 10 bits (or 1024 possible values). As
is apparent upon visual analysis of the third waveform, the
dominant frequency component has a wavelength of approximately 20
samples, which corresponds to the dominant frequency of 12.5 Hz.
For sleep staging, a filtering operation may be performed in the
sensing front end 512 before the analog-to-digital converters 612
(FIG. 6). For example, in sleep staging applications, a band pass
filter may be set in the range of about 0.5 Hz to about 20 Hz.
[0083] With respect to sleep staging, exemplary waveforms generally
associated with a sleeping person are shown in FIGS. 9B-9D. In FIG.
9B, an ECoG tracing shows a slow delta wave 916 associated with
sleep, FIG. 9C shows a theta wave 918, and FIG. 9D shows an alpha
wave 920. Each waveform is approximately three seconds long. If a
sampling rate of 250 Hz is used, each waveform results in
approximately 750 samples. The dominant frequency component in each
of FIGS. 9B, 9C, and 9D is approximately 2.5 Hz, 5.0 Hz and 10 Hz,
respectively.
Method for Detecting Sleep Stages
[0084] Sleep is characterized by alternating periods of highly
active rapid eye movement episodes (REM) and quiet non-REM (NREM)
episodes, which are characterized by increased power in the
low-frequency bands of the detectable EEG or EcoG waveforms. The
implantable devices described here are capable of distinguishing
between six discrete states or stages of sleep depth: wake (W),
rapid eye movement (REM), stage 1 (S1), stage 2 (S2), stage 3 (S3),
and stage 4 (S4), where S1 is a state of drowsiness or transition
between wake and sleep, S2 is a state of light sleep, and S3 and S4
are two different stages characteristic of deep sleep. The
implantable devices may include one or more sensors configured to
sense (continuously or intermittently) the electrical activity of
the brain or other physiological information. In some instances,
the implantable devices may include at least two sensors for
sensing physiological information. When at least two sensors are
employed, the sensors may sense the same or different types of
physiological information.
[0085] There are specific, well-known waveforms associated with EEG
signals recorded during different sleep states and stages. For
example, alpha, beta, theta, and delta waves are distinguished by
the frequency ranges over which they occur and/or amplitude (alpha:
8-12 Hz, 20-100 microvolts; beta: 14-30 Hz and low voltage, on the
order of 20 microvolts; theta: 4-7.5 Hz, usually under 20
microvolts; and delta: 0.5-3.5 Hz). Certain waveform morphology is
also characterized by the term "k-complexes" (an EEG wave having a
sharp negative front followed by a positive component) and "sleep
spindles" (bursts of 12-14 Hz activity often occurring with a
k-complex). There is a mix of alpha, beta, and higher frequency
waveforms during wake. During Stage 1, alpha activity drops to less
than 50% of the total power of the signal. and transitions to
theta. Stage 1 is usually brief, lasting about one to about seven
minutes. Stage 2 is predominantly associated with theta activity
with little to no alpha activity. Delta activity usually appears in
less than 20% of the EEG record monitored during Stage 2 sleep. The
amplitude of the waveforms may also increase from Stage 1. With
respect to Stage 3, delta activity with high peak-to-peak
amplitudes for about 20% to about 50% of the epoch, k-complexes,
and spindles may be seen, and during Stage 4, slow delta activity
for more than about 50% of the epoch may be seen. REM is associated
with mixed frequency waveforms, slow alpha activity, and alpha
spindles shorter than three seconds.
[0086] The method for detecting sleep stages disclosed herein is
related to the Gotman system of analyzing EEG waveforms (J. Gotman,
Automatic Seizure Detection: Improvements and Evaluation,
Electroencephalogr. Clin. Neurophysiol. 76(4):317-324 (1990)), as
previously described in U.S. Pat. No. 6,810,285 to Pless et al.,
which is hereby incorporated by reference in its entirety. In the
Gotman system, EEG waveforms are filtered and decomposed into
"features" representing characteristics of interest in the
waveforms. One such feature is characterized by the regular
occurrence (i.e., density) of half waves that exceed a threshold
amplitude occurring in a specified frequency band, especially in
comparison to background activity.
[0087] Analyzing half wave activity can be used to detect when
certain sleep stages are occurring. Much of the processing
performed by the implantable devices described here involves
operations on digital data in the time domain. To reduce the amount
of data processing required by the implantable devices, preferably
samples at ten-bit resolution are taken at a rate less than or
equal to approximately 500 Hz (2.0 ms per sample).
[0088] Referring now to FIG. 10A, the processing of the wave
morphology analysis units 712 is described in conjunction with a
filtered and sampled waveform 1010 of the type illustrated as the
bottom waveform 914 shown in FIG. 9A. In a first half wave 1012,
which is partially illustrated in FIG. 10A (the starting point
occurs before the illustrated waveform segment 1010 begins), the
waveform segment 1010 is essentially monotonically decreasing,
except for a small first perturbation 1014. Accordingly, the first
half wave 1012 is represented by a vector from the starting point
(not shown) to a first local extremum 1016, where the waveform
starts to move in the opposite direction. The first perturbation
1014 is of insufficient amplitude to be considered a local
extremum, and is disregarded by a hysteresis mechanism (discussed
in further detail below). A second half wave 1018 extends between
the first local extremum 1016 and a second local extremum 1020.
Again, a second perturbation 1022 is of insufficient amplitude to
be considered an extremum. Likewise, a third half wave 1024 extends
between the second local extremum 1020 and a third local extremum
1026; this may appear to be a small perturbation, but is greater in
amplitude than a selected hysteresis threshold. The remaining half
waves 1028, 1030, 1032, 1034, and 1036 are identified analogously.
As will be discussed in further detail below, each of the
identified half waves 1012, 1018, 1024, 1028, 1030, 1032, 1034, and
1036 has a corresponding duration 1038, 1040, 1042, 1044, 1046,
1048, 1050, and 1052, respectively, and analogously, a
corresponding amplitude determined from the relative positions of
each half wave's starting point and ending point along the vertical
axis, and a slope direction, either increasing or decreasing.
[0089] In one variation, the method allows for a programmable
hysteresis setting in identifying the ends of half waves. In other
words, as explained above, the end of an increasing or decreasing
half wave might be prematurely identified as a result of
quantization (and/or other noise, low-amplitude signal components,
and other perturbing factors), unless a small hysteresis allowance
is made before the reversal of the waveform direction is recognized
and a corresponding half wave end is identified. Hysteresis allows
for insignificant variations in signal level that are inconsistent
with the signal's overall movement to be ignored without the need
for extensive further signal processing such as filtering. Without
hysteresis, such small and insignificant variations might lead to
substantial and gross changes where half waves are identified,
leading to unpredictable results.
[0090] In one variation of sleep stage detection, if both the
amplitude and duration qualify by exceeding a corresponding preset
minimum threshold and qualify by not exceeding a preset maximum
threshold, then the amplitude, duration, half-wave time, and
half-wave direction are stored in a buffer. Adding a maximum
half-wave width parameter can further specify the minimum frequency
in a range of frequencies. A maximum amplitude threshold allows the
exclusion of high amplitude half waves that are often
characteristic of seizures or other non-sleep activity. For
example, as shown in FIG. 10B, the half wave with an amplitude 1055
and duration 1058 may be classified as a theta-type half wave,
whereas a half wave with an amplitude 1056 and duration 1059 may be
classified as a delta-type half wave. The half wave with amplitude
1057 and duration 1060 may be not satisfy the necessary amplitude
criterion so it is not included in any histogram that might be
generated using the data acquired or processed.
[0091] Processing that can be performed with regard to the waveform
1010 and half waves shown in FIG. 10A and 10B are illustrated in a
flow chart in FIG. 11. First, an increasing half wave is identified
(i.e., a half wave with an ending amplitude that is higher than its
starting amplitude), as in the second half wave 1018 of FIG. 10A).
To accomplish this, initial values are assigned to several
variables, namely, half-wave time (1110) (half-wave time
corresponds to the time that elapses between adjacent qualifying
half waves.) half-wave duration, peak amplitude, first sample
value, and ending threshold (1112) Specifically, the half-wave time
and half-wave duration value is set to zero; the peak amplitude and
first sample values are set to the amplitude value of the
last-observed sample (which, as described above, is a value having
10-bit precision); and the ending threshold is set to the last
observed sample minus a small preset hysteresis value.
[0092] When an EEG sample is acquired (1114), the half-wave time
and half-wave duration variables are incremented (1116). If
amplitude of the EEG sample is greater than the value of the peak
amplitude variable (1118), then the amplitude of the half wave is
increasing. If the half wave is increasing, the peak amplitude
variable is reset to correspond to the amplitude of the EEG signal
and the ending threshold is reset to a value corresponding to the
amplitude of the EEG signal less a predetermined small hysteresis
value, and the next EEG sample is awaited (1114). If the amplitude
of the EEG sample is less than the ending threshold (1122), then
the hysteresis value has been exceeded, and a local extremum has
been identified. Accordingly, the end of the increasing half wave
has been reached. At this point, the amplitude and duration of the
half wave are calculated (1124). The amplitude of the half wave is
the peak amplitude minus the amplitude of the first EEG sample
value, and the duration is simply the duration of the detected half
wave. If the local extremum is not yet identified, the next EEG
sample is awaited and the process is repeated on that sample
(1114). In one variation, if both the amplitude and the duration
qualify by both exceeding corresponding preset minimum thresholds
but not exceeding preset maximum thresholds (1126), then the
amplitude, duration, half-wave time, and half-wave direction
(increasing) are stored in a buffer (1128), and the half-wave time
is reset to zero (1130). However, in other variations (not shown),
multiple amplitude and duration threshold comparisons might be
simultaneously performed on the same half wave to simultaneously
determine delta, theta, and alpha components. For example, and
referring now to FIG. 10B, a half wave with illustrated amplitude
1056 and duration 1059 may not satisfy the criterion for alpha, but
may satisfy the criterion for theta. The parameters for this half
wave may then be stored in a buffer for theta. Multiple buffers may
also be provided to store parameters, e.g., if different frequency
bands are employed. In another variation, multiple half-wave
detectors may be configured to run in parallel so that parameters
for each detector correspond to a specific frequency band.
[0093] Once an increasing half wave is detected, the half-wave
duration, the peak amplitude, the first sample value, and ending
threshold variables are initialized again (1132). Half-wave
duration is set to zero, the peak amplitude and the first sample
value are set to the most recent sample value, and the ending
threshold is set to the last sample value plus the hysteresis value
and the next EEG sample is awaited (1134), the half-wave time and
half-wave duration variables are incremented (1136). If the
amplitude of the EEG sample is lower than the peak amplitude
(1138), then the direction of the half wave is decreasing.
Accordingly, the-peak amplitude is reset to the amplitude of the
current EEG sample, and the ending threshold is reset to correspond
to the amplitude of the current EEG sample plus the hysteresis
value (1140), and the next sample is awaited (1134).
[0094] If the amplitude of current EEG sample is greater than the
ending threshold (1142), then the hysteresis value has been
exceeded, and a local extremum has been identified. Accordingly,
the end of the decreasing half wave has been reached, and the
amplitude and duration of the half wave are calculated (1144). The
amplitude is the first sample value minus the peak amplitude, and
the duration is the duration of the half wave in the current EEG
sample. If the extremum is not identified, the next EEG sample is
awaited (1134). If both the amplitude and the duration qualify by
exceeding corresponding preset minimum thresholds but not exceeding
a preset maximum thresholds (1146), then the amplitude, duration,
half-wave time, and half-wave direction (decreasing) are stored in
a buffer (1148), and the half-wave time is reset to zero
(1150).
[0095] Once a decreasing half wave is identified, the
above-described operations are repeated to identify additional half
waves (1112). Parameters corresponding to qualifying half waves,
including their directions, durations, amplitudes, and the elapsed
time between adjacent qualified half waves (i.e., the half-wave
time variable) are stored. Half wave detection is an ongoing and
continuous process, but the half wave detection operations may be
suspended from time to time when conditions or device state call
for it, e.g. when the implantable device is inactive or, if the
implantable device is configured to deliver stimulation or some
other therapy, when the stimulation or the other therapy is being
performed. The half wave detection procedure can be resumed after
it has been suspended at the first initialization operation
(1110).
[0096] In one variation, to reduce power consumption, the half wave
detection operations are performed in customized electronic
hardware. Preferably, the operations of FIG. 11 are performed in
parallel whenever the wave morphology analysis units 712 (FIG. 7)
are active
[0097] Software operations can also be carried out on a periodic
basis to provide useful information about the physiological
information being monitored. In one variation, and referring now to
FIG. 12, the stored information associated with the qualifying half
waves is processed to define sleep states or stages by the ratio or
absolute quantity of delta, theta or alpha waves detected within a
certain period of time. More specifically, the software process
involves clearing a half-wave window flag (1210), identifying the
qualifying half waves detected during the chosen period of time
(1212), associating a "current half wave" variable with the oldest
detected half wave in the chosen period of time (1214), and using a
histogram counter to track the time interval between the most
recent half wave detected and prior detected half waves (1216). The
chosen period of time can be a defined processing window (e.g., a
recurring time interval that is either fixed or programmable). For
example, a 128 ms processing window may be selected which, at a 250
Hz sampling rate, would be expected to correspond to 32 EEG
samples.
[0098] The histogram counter tracks the time interval between the
most recent half wave detected and the prior half waves are then
measured, where x1 is the number of half waves to be identified
within a selected half-wave time window (the duration of the
half-wave time window is another programmable value) for the first
event detector 812, and x2 is the number of half waves within the
same half-wave time window for the second event detector 816, and
likewise for x3 and the third event detector 822. Detection of an
event may be defined as the occurrence of the condition when x1 is
greater than a prespecified value, or when the ratio of any
combination of x (e.g., x1 and x2) is greater than a prespecified
value.
[0099] A histogram counter may be included in the detection
subsystem (FIG. 4) to keep track of the detections within each
frequency band, providing a measure of the average low-frequency
power in the signal over a period of time. The number detections
within each frequency band over a specified time window (for
example, a sliding window of 1-10 minutes) then could be used to
classify which sleep stage occurred for that time period.
Transitions are noted when the number of detections within one band
decreases and in another band increases.
[0100] Sleep stages may be detected by using a set of low frequency
half-wave counters simultaneously detecting waveforms in the EEG
samples. For example, these counters may be set up to detect the
waveform activity in the frequency range of 0.5 Hz-3.5 Hz for the
delta band, 4.0 Hz-7.5 Hz for the theta band, and 8.0 Hz-12 Hz for
the alpha band. Line length and area detectors may also be
implemented to measure the overall complexity and power of the
signal. Sleep stages may then be characterized by the ratio or
absolute quantity of delta, theta, alpha within a period of time.
For example, in the histogram shown in FIG. 23, the number of delta
half wave counts (on the y-axis) are binned into brief time windows
(on the x-axis) between about 10 seconds to about one minute. When
the number of half waves within a window exceeds a threshold x, a
particular sleep stage may be detected. For example, if between
about 10 and about 25 half waves are detected in a 10 second
window, then sleep Stage 3 is detected; and if greater than 25
delta half waves are detected per 10 second window, then sleep
Stage 4 is detected. REM, wake, and sleep Stages 1 and 2 may be
determined in a similar manner by characterization of alpha and
theta waves. The thresholds may be independently adjusted to
optimally tune the sleep stage detector to each individual patient.
Algorithms, such as one that calculates the ratio between different
frequency bands (e.g., the ratio between delta and theta), may also
be employed.
[0101] The event detectors detectors may be configured in any
manner suitable to monitor sleep or detect sleep states or stages.
In one variation, a single wave morphology analysis unit is used to
detect half waves. In another variation, where multiple waveform
morphology analysis are used, they may be set up in parallel to
each frequency band of interest. Half waves within a specific
duration and amplitude range may then be binned into appropriate
bins in a histogram.
[0102] The time interval between the most-recently detected half
wave and x previously-detected half waves is then tested against a
selected half-wave time window (1216), where x is a specified
minimum number of half waves (preferably a programmable value) to
be identified within and the half-wave time window The half-wave
time window is desirably selected to correspond to the time over
which detection of a certain sleep state or stage, or might be
expected to occur. If the measured time interval is less than the
half-wave time window (1218), then the half-wave window flag is set
(1220). Logic inversion is then selectively applied (1222) to
determine whether a wave morphology analysis unit (or other
analyzer) is triggered by the presence or absence of a condition,
as explained in greater detail below. If the measured time interval
is greater than or equal to the half-wave time window, the value of
the half wave pointer is incremented to point to the next new half
wave (1228) If there are no more new half waves (1230), logic
inversion is applied if desired (1222), and the procedure ends
(1224). If there are more new half waves, the next time interval is
tested (1216) and the process continues from there.
[0103] Logic inversion allows the output flag for the wave
morphology analysis unit (or any other analyzer) to be selectively
inverted. If logic inversion is applied to an output of a
particular analysis unit, then the corresponding flag will remain
cleared when the detection criterion (e.g., number of qualifying
half waves) is met, and will be set when the detection criterion is
not met. This capability provides some additional flexibility in
configuration, facilitating detection of the absence of certain
signal characteristics when, for example, the presence of those
characteristics is the norm.
[0104] In one variation, the half-wave window flag is used to
indicate whether the number of qualifying half waves exceeding a
predetermined value has occurred over an interval the endpoint of
which corresponds to the end of the most recent processing window.
To reduce the occurrence of spurious detections, an X-or-Y
criterion is applied to prevent triggering the wave morphology
analysis unit unless a sufficient number of qualifying half waves
occur in X of the Y most recent processing windows, where X and Y
are parameters individually adjustable for each analysis unit.
Referring now to FIG. 13, a sum representing recent processing
windows in which the half-wave window flag was set is cleared to
zero and a current window pointer is initialized to point to the
most recent processing window (1310). If the half-wave window flag
corresponding to the current window pointer is set (1312), then the
sum is incremented (1314). If there are more processing windows to
examine (for an X-of-Y criterion, a total of Y processing windows,
including the most recent, should be considered) (1316), then the
window pointer is decremented (1318) and the flag testing and sum
incrementing steps (1312-1314) are repeated. After Ywindows have
been considered, if the sum of windows having set half-wave window
flags meets the threshold X (1320), then the half-wave analysis
flag is set (1322), persistence (described below) is applied
(1324), and the procedure is complete. Otherwise, the half wave
analysis flag is cleared (1326).
[0105] Persistence, another per-analysis-tool setting, allows the
effect of an event detection (a flag set) to persist beyond the end
of the detection window in which the event occurs. Persistence may
be set anywhere from one to fifteen seconds (though other settings
are possible), so if detections with multiple analysis tools (e.g.,
multiple wave morphology analysis units or multiple event
detectors) do not all occur simultaneously (though they should
still occur within a fairly short time period), a Boolean
combination of flags will still yield positive results. Persistence
can also be used with a single analysis tool to smooth the
results.
[0106] When the process of FIG. 13 is completed, the half-wave
analysis flag (set or cleared in steps 1322 and 1326, respectively)
indicates whether an event has been detected in the corresponding
channel of the wave morphology analysis units 712 or, stated
another way, whether a sufficient number of qualifying half waves
have appeared in X of the Y most recent processing windows.
Although in the variations shown in FIGS. 12 and 13, the operations
are performed in software, it should be recognized that some or all
of those steps can be performed using customized electronics, if it
proves advantageous in the desired application to use such a
configuration (e.g., to optimize the computational power that is
required to carry out the operations in the implantable
device).
[0107] In some instances, it may be desirable to include either or
both a line length detector and an area detector for sleep staging.
For example, these features may be useful when the amplitude of the
EEG sample is high and/or the signal is complex, as may occur
during the REM stage or during deep sleep or "slow wave" sleep
(Stages 3 and 4). In the instances of increasing amplitude, the
high amplitude of the slow wave may be detected using an area
detector alone or in combination with a half wave detector.
Similarly, in REM sleep there may be an increase in signal
complexity that can be detected using a line length detector alone
or in conjunction with a half wave detector.
[0108] FIG. 14 illustrates the filtered waveform of FIG. 9A,
further depicting line lengths identified within a time window. The
time window selected may be the same as or different from the
selected half-wave processing window. From an implementation
standpoint, a single device interrupt upon the conclusion of each
processing window allows all of the analysis tools to perform the
necessary corresponding software processes; the line length
analysis process of FIG. 16 (described below) is one such example.
The waveform 1410 shown in FIG. 14 is a filtered and otherwise
preprocessed EEG signal as received by one of the window analysis
units 714 from the sensing front end 512. As discussed above, line
lengths are considered within time windows. The duration of the
time window 1412 shown in FIG. 14 is 128 ms which, at a 250 Hz
sampling rate corresponds to an expected 32 samples. Other time
windows and sampling rates can be specified as appropriate.
[0109] The total line length for the time window 1412 is the sum of
the sample-to-sample amplitude differences within that window 1412.
For example, the first contribution to the line length within the
window 1412 is a first amplitude difference 1414 between a previous
sample 1416 occurring immediately before the window 1412 and a
first sample 1418 occurring within the window 1412. The next
contribution comes from a second amplitude difference 1420 between
the first sample 1418 and a second sample 1422; a further
contribution 1424 comes from a third amplitude difference between
the second sample 1422 and a third sample 1426, and so on. At the
end of the window 1412, the final contribution to the line length
comes from a last amplitude difference 1430 between a second-last
sample 1432 in the window 1412 and a last sample 1434 in the window
1412. Note that all line lengths, whether increasing or decreasing
in direction, are accumulated as positive values. A decreasing
amplitude difference 1414 and an increasing amplitude difference
1428 therefore both contribute to a greater line length.
[0110] The flow chart of FIG. 15 describes how the line length is
calculated. At the beginning of a time window, a "line length
total" variable is initialized to zero (1510). An EEG sample is
awaited (1512), and the absolute value of the difference in
amplitude between the current EEG sample and the previous EEG
sample (which, when considering the first sample in a window, may
come from the last sample in a previous window) is measured (1514).
Then, the previous sample (if any) is replaced with the value of
the current sample (1516), and the calculated absolute value is
added to the total (1518). If there are more samples remaining in
the window 1412 (1520), another current sample is awaited (1512)
and the method continues. Otherwise, the line length calculation
for the window 1412 is complete, and the total is stored (1522),
the total is reinitialized to zero (1510), and the method
continues.
[0111] In other variations, either the measured amplitude
difference (calculated as described above (1514)) or the sample
values used to calculate the measured amplitude difference may be
mathematically transformed in useful nonlinear ways. For example,
it may be advantageous in certain circumstances to calculate the
difference between adjacent samples using the squares of the sample
values, or to calculate the square of the difference between sample
values, or both. It is contemplated that other transformations
(such as square root, exponentiation, logarithm, and other
nonlinear functions) might also be advantageous in certain
circumstances. Whether such a transformation is performed and the
nature of any transformation to be performed preferably are
programmable parameters of the implantable device 110.
[0112] As with the half wave analysis method set forth above, the
line length calculation does not need to terminate, rather, it can
be configured to be free-running yet interruptible. If the line
length calculation is restarted after having been suspended, it
should be reinitialized and restarted at the beginning of a time
window. This synchronization maybe accomplished through hardware
interrupts.
[0113] Referring now to the flow chart of FIG. 16, the calculated
line lengths are further processed after the calculations for each
time window 1412 are obtained and stored. The process begins by
calculating a running accumulated line length total over a period
of n time windows. Where n>1, the effect is that of a sliding
window. (Indeed, in one variation, true sliding-window processing
is used). The accumulated total variable is first initialized to
zero (1610). A current window pointer is set to indicate the nth to
the last window, i.e., the window (n-1) windows before the most
recent window (1612). The line length of the current window is
added to the total (1614), the current window pointer is
incremented (1616), and if there are more windows between the
current window pointer and the most recent (last) window (1618),
the adding and incrementing steps (1614-1616) are repeated.
Accordingly, by this method, the resulting total includes the line
lengths for each of the n most recent windows.
[0114] In one variation, the accumulated total line length is
compared to a dynamic threshold, which is based on a trend of
recently observed line lengths. The trend is recalculated regularly
and periodically, after each recurring line-length trend interval
(which is preferably a fixed or programmed time interval). Each
time the line-length trend interval passes (1620), the line length
trend is calculated or updated (1622). In one variation, this is
accomplished by calculating a normalized moving average of several
trend samples, each of which represents several consecutive windows
of line lengths. A new trend sample is taken and the moving average
is recalculated upon every line length trend interval. The number
of trend samples used in the normalized moving average and the
number of consecutive windows of line length measurements per trend
sample both are preferably fixed or programmable values.
[0115] After the line length trend has been calculated, the line
length threshold is calculated (1624) based on the new line length
trend. In The threshold may be set as either a percentage of the
line length trend (either below 100% for a threshold that is lower
than the trend or above 100% for a threshold that is higher than
the trend) or alternatively as a fixed numeric offset from the line
length trend (either negative for a threshold that is lower than
the trend or positive for a threshold that is higher than the
trend). It should be observed that other methods for deriving a
numeric threshold from a numeric trend are possible.
[0116] The first time the process of FIG. 16 is performed, there is
generally no line length trend against which to set a threshold.
Accordingly, for the first several passes through the process
(until a sufficient amount of EEG data has been processed to
establish a trend), the threshold is essentially undefined and the
line length detector should not return a positive detection. Some
"settling time" is required to establish trends and thresholds
before a detection can be made. If the accumulated line length
total exceeds the calculated threshold (1626), then a flag is set
(1628) indicating a line-length-based event detection on the
current window analysis unit channel 714. As described above, the
threshold is dynamically calculated from a line length trend, but
alternatively, the threshold may be static, either fixed or
programmed into the implantable device 110. If the accumulated line
length total does not exceed the threshold, the flag is cleared
(1630).
[0117] Once the line length flag has been either set or cleared,
logic inversion is applied (1632), persistence is applied (1634),
and the procedure terminates. The resulting persistent line length
flag indicates whether the threshold has been exceeded within one
or more windows over a time period corresponding to the line length
flag persistence. As will be discussed in further detail below,
line length event detections can be combined with the half-wave
event detections, as well as any other applicable detection
criteria.
[0118] FIG. 17 illustrates the filtered waveform of FIG. 9A with
area under the curve identified within a window. Area under the
curve, which in some circumstances is somewhat representative of a
signal's energy (though energy of a waveform is more accurately
represented by the area under the square of a waveform), may be
another criterion for detecting certain events occurring with
respect to the physiological information being monitored by the
implantable device. In FIG. 17, the total area under the curve
represented by a waveform 1710 within the window 1712 is equal to
the sum of the absolute values of the areas of each rectangular
region of unit width vertically bounded by the horizontal axis and
the EEG sample. For example, the first contribution to the area
under the curve within the window 1712 comes from a first region
1714 between a first sample 1716 and a baseline 1717. A second
contribution to the area under the curve within the window 1712
comes from a second region 1718, including areas between a second
sample 1720 and the baseline 1717. There are similar regions and
contributions for a third sample 1722 and the baseline 1717, a
fourth sample 1724 and the baseline 1717, and so on. (From a
mathematical standpoint, the region widths are not significant to
the area calculation; rather, the area can be considered the
product of the amplitude or the region and a region unit width,
which can be disregarded.) Although the concept of separate
rectangular regions is a useful construct for visualizing the idea
of area under a curve, any process for calculating area need not
partition areas into regions as such regions are shown in FIG.
17.
[0119] Referring now to the flow chart of FIG. 18, the areas under
the curve shown in the graph of FIG. 17 are calculated using a
process that is invoked at the beginning of a time window.
Initially, an "area total" variable is initialized to zero (1810).
The current EEG sample is awaited (1812), and the absolute value of
the current EEG sample is measured (1814). As with the line length
calculation method described above in other variations, the current
EEG sample may be mathematically transformed in useful nonlinear
ways. For example, it may be advantageous in certain circumstances
to calculate the square of the current sample rather than its
absolute value. The result of such a transformation by squaring
each sample will generally be more representative of signal energy,
though it is contemplated that other transformations (such as
square root, exponentiation, logarithm, and other nonlinear
functions) might also be advantageous in certain circumstances.
Whether such a transformation is performed and the nature of any
transformation performed both preferably are programmable
parameters of the implantable device 110.
[0120] The calculated absolute value is added to the total (1816).
If there are more EEG samples remaining in the window 1712 (step
1818), another current sample is awaited (1812) and the process
continues. Otherwise, the area calculation for the window 1712 is
complete, and the total is stored (1820), the total is
reinitialized to zero (1810), and the method continues. As with the
half wave and line length analysis methods set forth above, the
area calculation does not need to terminate, rather, it can be
configured to be free-running yet interruptible. If the area
calculation is restarted after having been suspended, it should be
reinitialized and restarted at the beginning of a window. This
synchronization can be accomplished through hardware
interrupts.
[0121] Referring now to the flow chart of FIG. 19, the area
calculations are processed. As is accomplished for the line length
calculations, the area detection method begins by calculating a
running accumulated area total over a period of n time windows.
Again, where n>1, the effect is that of a sliding window. The
accumulated total is initialized to zero (1910). A current window
pointer is set to indicate the nth to the last window, i.e., the
window (n-1) windows before the most recent window (1912). The area
for the current window is added to the total (1914), the current
window pointer is incremented (1916), and if there are more windows
between the current window and the most recent (last) window
(1918), the adding and incrementing steps (1914-1916) are repeated.
Accordingly, by this process, the resulting total includes the
areas under the curve for each of the n most recent windows.
[0122] In one variation, the accumulated total area is compared to
a dynamic threshold, which is based on a trend of recently observed
areas. The trend is recalculated regularly and periodically, after
each recurring area trend interval (which is preferably a fixed or
programmed time interval). Each time the area trend interval passes
(1920), the area trend is calculated or updated (1922). In one
variation, this is accomplished by calculating a normalized moving
average of several trend samples, each of which represents several
consecutive windows of areas. A new trend sample is taken and the
moving average is recalculated upon every area trend interval. The
number of trend samples used in the normalized moving average and
the number of consecutive windows of area measurements per trend
sample both are preferably fixed or programmable values.
[0123] After the area trend has been calculated, the area threshold
is calculated (step 1924) based on the new area trend. As with line
length, discussed above, the threshold may be set as either a
percentage of the area trend (either below 100% for a threshold
that is lower than the trend or above 100% for a threshold that is
higher than the trend) or alternatively a fixed numeric offset from
the area trend (either negative for a threshold that is lower than
the trend or positive for a threshold that is higher than the
trend).
[0124] Again, as is the case with the line length detection method,
the first time the process described in FIG. 19 is performed, there
is generally no area trend against which to set a threshold.
Accordingly, for the first several passes through the process
(until a sufficient amount of EEG data has been processed to
establish a trend), the threshold is essentially undefined and the
area detector should not return a positive detection. Some
"settling time" is required to establish trends and thresholds
before a detection can be made.
[0125] If the accumulated total exceeds the calculated threshold
(1926), then a flag is set (1928) indicating an area-based event
detection on the current window analysis unit channel 714.
Otherwise, the flag is cleared (1930). Once the area flag has been
either set or cleared, logic inversion is applied (1932),
persistence is applied (1934), and the procedure terminates. The
resulting persistent area flag indicates whether the threshold has
been exceeded within one or more windows over a time period
corresponding to the area flag persistence. As will be discussed in
further detail below, area event detections can be combined with
the half-wave event detections, line-length event detections, as
well as any other applicable detection criteria described
herein.
[0126] In one variation, each threshold for each channel and each
analysis tool can be programmed separately. ("Tool" as used herein
refers to an aspect of a unit, for example, the window analysis
unit includes the line length analysis tool and the area analysis
tool.) Therefore, a large number of individual thresholds may be
used in the method for monitoring physiological information
relating to sleep with an implantable device 110. It should be
noted thresholds can vary widely and they can be changed and/or
updated by a physician to meet the needs of an individual patient
via the external programmer 312 (FIG. 3), and some analysis unit
thresholds (e.g., line length and area) can also be automatically
varied depending on observed trends in the data. This is preferably
accomplished based on a moving average of a specified number of
window observations of line length or area, adjusted as desired via
a fixed offset or percentage offset, and may compensate to some
extent for diurnal and other normal variations in brain
electrophysiological parameters.
[0127] The methods described by the flow charts of FIGS. 11-13,
15-16, and 18-19 can be implemented in different ways. For example,
state machines, software, hardware (including ASICs, FPGAs, and
other custom electronics), and various combinations of software and
hardware, are all solutions that would be possible to practitioners
of ordinary skill in the art of electronics and systems design.
Since minimizing the computational load on the processor is often
desirable, certain operations can be implemented using hardware or
a combination of hardware and software, rather than software
alone.
[0128] As described previously in connection with FIG. 13, one of
the detection schemes set forth above (i.e., half wave detection)
is adapted to use an X-of-Y criterion to weed out spurious
detections. This can be implemented via a shift register or,
alternatively, by more efficient computational methods. Half waves
can be analyzed on a window-by-window basis, and the window results
can be updated with respect to a separate analysis window interval.
If the detection criterion (i.e., a certain number of half waves in
less than a specified time period) is met for any of the half waves
occurring in the most recent window, then detection is satisfied
within that window. If that occurs for at least X of the Y most
recent windows, then this indicates that the half wave detection
sought was, in fact, detected. If desired, X-of-Y criterion can be
used with other detection algorithms (such as line length and area
such that if thresholds are exceeded in at least X of the Y most
recent windows, then the corresponding analysis unit triggers a
detection. Also, in the described variations, each detection flag,
after being set, remains set for a selected amount of time,
allowing them to be combined by Boolean logic (as described below)
without necessarily being simultaneous.
[0129] As indicated above, each of the software processes set forth
above (FIGS. 12-13, 16, and 19) correspond to functions performed
by the wave morphology analysis units 712 and window analysis units
714. Each one is initiated periodically, typically once per a
window with a length that is predetermined (1212, 1512). The
outputs from the half wave and window analysis units 712 and 714,
namely the flags generated in response to counted qualifying half
waves, accumulated line lengths, and accumulated areas, are
combined to identify event detections as functionally illustrated
in FIG. 8 and as described via flow chart in FIG. 20.
[0130] The process begins with the receipt of a timer interrupt
(2010), which is typically generated on a regular periodic basis to
indicate the edges of successive time windows. Accordingly, in a
method according to the disclosed embodiment of the invention, such
a timer interrupt is received every 128 ms, or as otherwise
programmed or designed. Then the latest data from the half wave
(2012, FIGS. 12-13), line length (2014, FIG. 16), and area (2016,
FIG. 19) detection processes are evaluated, via the half-wave
analysis flag, the line-length flag, and the area flag for each
active sensing channel. The steps of checking the analysis tools
(2012, 2014, and 2016) can be performed in any desired order or in
parallel, as they are generally not interdependent. It should be
noted that the foregoing analysis tools should be checked for every
active channel (i.e., channels on which data is currently being
sensed and/or processed), and may be skipped for inactive channels
(i.e., detection channels not currently in use).
[0131] Flags that indicate whether particular signal
characteristics have been identified in each active channel for
each active analysis tool are then combined into detection channels
(2018) as illustrated in FIG. 8. This operation is performed as
described in detail below with reference to FIG. 21. Each detection
channel is a Boolean "AND" combination of analysis tool flags for a
single channel, and as disclosed above, there may be at least eight
channels. The flags for multiple detection channels are then
combined into event detector flags (2020), which are indicative of
identified neurological events calling for action by the device. As
shown in FIG. 20, if an event detector flag is set (2022), then a
corresponding action is initiated (2024) by the device. Actions
according to the invention can include the presentation of a
warning to the patient, an application of therapeutic electrical
stimulation, a delivery of a dose of a drug, an initiation of a
device mode change, or a recording of certain EEG signals. It will
be appreciated that there are numerous other possibilities. It is
preferred, but not necessary, for actions initiated by a device
according to the invention to be performed in parallel with the
sensing and detection operations that are described in detail
herein. It should be recognized that the application of electrical
stimulation to the brain may require suspension of certain of the
sensing and detection operations, as electrical stimulation signals
may otherwise feed back into the detection system 422 (FIG. 4),
causing undesirable results and signal artifacts.
[0132] Multiple event detector flags are possible, each one
representing a different combination of detection channel flags. If
there are further event detector flags to consider (2026), those
event detector flags are also evaluated (2022) and may cause
further actions by the device (2024). It should be noted that, in
general, actions performed by the device (as in 2024) may be in
part dependent on a device state. For example, even if certain
combinations of events do occur, no action may be taken if the
device is in an inactive state.
[0133] As described above, and as illustrated in FIG. 20 as step
2018, a corresponding set of analysis tool flags is combined into a
detection channel flag as shown in FIG. 21 (see also FIG. 8).
Initially the output detection channel flag is set (2110).
Beginning with the first analysis tool for a particular detection
channel (2112), if the corresponding analysis tool flag is not set
(2114), then the output detection channel flag is cleared
(2116).
[0134] If the corresponding analysis tool flag is set (2114), the
output detection channel flag remains set, and further analysis
tools for the same channel, if any (2118), are evaluated.
Accordingly, this combination procedure operates as a Boolean "AND"
operation. That is, if any of the enabled and active analysis tools
for a particular detection channel does not have a set output flag,
then no detection channel flag is output by the procedure.
[0135] An analysis tool flag that is cleared indicates that no
detection has been made within the flag persistence period and, for
those analysis tools that employ an X-of-Y criterion, that such
criterion has not been met. In certain circumstances, it may be
advantageous to also provide detection channel flags with logic
inversion. Where a desired criterion (i.e., combination of analysis
tools) is not met, the output flag is set (rather than cleared,
which is the default action). This can be accomplished by providing
selectable Boolean logic inversion (2120) corresponding to each
event detector.
[0136] Also as described above, and as illustrated in FIG. 20
(2020), multiple detection channel flags are combined into a single
event detector flag as shown in FIG. 22 (see also FIG. 8).
Initially the output event detector flag is set (2210). Beginning
with the first detection channel for a particular event detector
(2212), if the channel is not enabled (2214), then no check is
made. If the channel is enabled and the corresponding detection
channel flag is not set (2216), then the output event detector flag
is cleared (2218) and the combination procedure exits. If the
corresponding detection channel flag is set (2216), the output
event detector flag remains set, and further detection channels, if
any (2220), are evaluated after incrementing the channel being
considered (2222). This combination procedure also operates as a
Boolean "AND" operation, as if none of the enabled and active
detection channels has a set output flag, then no event detector
flag is output by the procedure. It should also be observed that a
Boolean "OR" combination of detection channels may provide useful
information in certain circumstances. A software or hardware flow
chart accomplishing such a combination is not illustrated, but
could easily be created by an individual of ordinary skill in
digital electronic design or computer programming.
[0137] In general, two different data reduction methodologies may
be used in sleep state or stage detection. Both methods collect
data representative of EEG signals within a sequence of uniform
time windows each having a specified duration. The first data
reduction methodology involves the calculation of a "line length
function" for an EEG signal within a time window. Specifically, the
line length function of a digital signal represents an accumulation
of the sample-to-sample amplitude variation in the EEG signal
within the time window. Stated another way, the line length
function is representative of the variability of the input signal.
A constant input signal will have a line length of zero
(representative of substantially no variation in the signal
amplitude), while an input signal that oscillates between extrema
from sample to sample will approach the maximum line length. It
should be noted that while the line length function has a
physical-world analogue in measuring the vector distance traveled
in a graph of the input signal, the concept of line length as
treated herein disregards the horizontal (X) axis in such a
situation. The horizontal axis herein is representative of time
which, mathematically is not currently believed to be combinable in
any meaningful way with information relating to the vertical (Y)
axis, generally representative of amplitude, and which in any event
would not be expected to contribute anything of interest
[0138] The second data reduction methodology involves the
calculation of an "area function" represented by an EEG signal
within a time window. Specifically, the area function is calculated
as an aggregation of the EEG's signal total deviation from zero
over the. time window, whether positive or negative. The
mathematical analogue for the area function defined above is the
mathematical integral of the absolute value of the EEG function (as
both positive and negative signals contribute to positive area).
Once again, the horizontal axis (time) makes no contribution to
accumulated energy as treated herein. Accordingly, an input signal
that remains around zero will have a small area value, while an
input signal that remains around the most positive or most negative
values will have a high area value.
[0139] Both the area and line length functions may undergo linear
or nonlinear transformations. An example would be to square each
amplitude before summing it in the area function. This nonlinear
operation would provide an output that would approximate the energy
of the signal for the period of time it was integrated. Similarly,
linear and nonlinear transformations of the difference between
sample values are advantageous in customizing the line length
function to increase the effectiveness of the implantable device
for a specific patient.
[0140] The central processing unit receives the line length
function and area function measurements performed by the detection
subsystem, and is capable of acting based on those measurements or
their trends. Feature extraction, specifically the identification
of half waves in an EEG signal, also provides useful
information.
[0141] The identification of half waves having specific amplitude
and duration criteria allows some frequency-driven characteristics
of the EEG signal to be considered and analyzed without the need
for computationally intensive transformations of time-domain EEG
signals into the frequency domain. Specifically, the half wave
feature extraction capability of the implantable devices described
herein identifies those half waves in the input signal having a
duration that exceeds a minimum duration criterion but does not
exceed a maximum duration criterion, and an amplitude that exceeds
a minimum amplitude criterion but does not exceed a maximum
amplitude criterion. The number of half waves in a time window
meeting those criteria is somewhat representative of the amount of
energy in a waveform at a frequency below the frequency
corresponding to the minimum duration criterion. Additionally, the
number of half waves in a time window is constrained somewhat by
the duration of each half wave (i.e., if the half waves in a time
window have particularly long durations, relatively fewer of them
will fit into the time window). That is, that number is highest
when a dominant waveform frequency most closely matches the
frequency corresponding to the minimum duration criterion.
[0142] As stated above, the half waves, line length function, and
area function of various EEG signals can be calculated by
customized electronics modules with minimal involvement by the
central processing unit, and are selectively combined in the
implantable device to provide detection and prediction of seizure
activity, so that appropriate action can then be taken.
[0143] A number of other more sophisticated signal processing tools
may be included in the device design in order to better detect
sleep stages. For example, implementing a Fast Fourier transform
(FFT) routine in the implantable device would allow a direct
measure of the power spectrum over time. Thresholds for power
within each low frequency band of interest could be set to identify
different stages of sleep. Information related to theoretic
measures such as entropy can also be used to measure different
stages of sleep, as the signal's complexity is inversely correlated
with the sleep depth. In addition, synchrony and mutual information
between channels can be used as a measure for determining sleep
stages, as these features have been shown to increase with the
depth of sleep.
[0144] Other analytical methods for monitoring physiological
information relating to sleep include developing individualized
detection sets that are based on each individual patient's EEG
signals This is important because the underlying neurophysiological
signals may vary from patient to patient due to electrode location,
disease, or pharmacological effects. To implement an individualized
detection set, the device may include algorithms for training a
model based on artificial neural networks (ANN) or by using Hidden
Markov Modeling. This analytical method would require additional
input from the user (e.g., the patient's physician or clinician),
including a training set of signals that have already been
characterized by the user.
[0145] Physiological information other than EEG or ECoG signals may
be used to provide supplementary information for sleep staging or,
in the absence of EEG and/or ECoG data, as the primary information
for sleep staging. For example signals from an intracranial sensor
that monitors of global cerebral blood flow (CBF) and/or cerebral
metabolic rate (CMR) can be incorporated into the implantable
device. Global CBF and CMR are reduced during light sleep (Stage 2)
compared to wake (3-10%), and even further reduced during deep
sleep (25-44%). In addition, if the CBF/CMR sensors are place
appropriately, localized information about CBF and CMR can be used
to monitor the patient's state. Other implementations may include
temperature, heart rate, position (including head position), EMG,
EOG, and body movement sensors.
[0146] Data about low-frequency detections and sleep stages will be
available to the physician when the device is interrogated. In
addition, detections may be used to dynamically update therapy
parameters in order to adjust therapy based on the patient's sleep
schedule and/or to adjust therapy based on the patient's sleep
state.
[0147] Sleep Staging Applications
[0148] Sleep stage information derived from the implantable device
may be used to determine the effectiveness of sleep therapies
and/or modulate therapy delivery (e.g., the implantable device may
be programmed to deliver a different therapy during sleep and wake
or during different sleep stages). In one variation, therapy may be
coordinated with the detection of a particular sleep stage. For
example, cardiac therapy may be implemented during a specific
vulnerable sleep stage to reduce the occurrence of arrhythmia or
ischemia. This information may also provide the patient, physician,
or caretaker with information about the patient's sleep quality
(e.g., duration of time to fall asleep, number of arousals from
sleep, duration of time in slow-wave sleep periods, and duration of
sleep cycles). A sleep quality index may also be generated to
indicate sleep quality. The sleep quality index may be determined
from a number of outputs derived from the sleep staging tools,
including total duration of sleep within per 24 hours, the number
of arousals per night, and the duration spent in each sleep
stage.
[0149] The sleep stage information detected by the implantable
devices herein described may also be used to treat various medical
conditions (allocate therapy). For example, it may be used to treat
neurological, psychological, cardiac, respiratory, and sleep
conditions, or a combination thereof. Specific neurological
conditions may include chronic pain, epilepsy, and movement
disorders such as Parkinson's disease, Tourette's disorder, tremor,
and restless leg syndrome. Psychiatric conditions such as
depression, anxiety, and bipolar disorder may also be treated.
Furthermore, the implantable devices may be used in methods of
treating sleep conditions such as sleep apnea and narcolepsy.
[0150] For example, in epilepsy, some seizures occur preferentially
during the drowsy state (Stage 1) or slow-wave sleep (Stage 3 and
Stage 4). Therefore, the sleep staging detections may be used to
modulate delivered therapy. For example, high frequency stimulation
may be delivered in response to paroxysmal events during Stage 3
and Stage 4 sleep, while low frequency stimulation may be delivered
during Stage 2 sleep, wake, and REM sleep, when seizures are less
likely to occur.
[0151] Similarly, sleep disturbances and or disruptions are often
associated with depression. An implantable sleep staging device may
be used to monitor sleep disruptions and provide feedback to the
physician about the patient's sleep quality as another metric of
the patient's overall health. In addition, there is often a
circadian mood cycle with depression, and an implantable device
that could detect sleep stages may be used to allocate therapy in
accordance to the patient's circadian cycle. For example, in
patients who have worsening of mood in the morning, increased
therapy may be provided in response to the detection of Stage 1 or
the transition from Stage 1 to wake. This may be preferable to a
scheduled increase in therapy dosage since patients may follow a
different sleep routine from day to day. In addition to treating
the mood symptoms, stimulation or drug therapy may also be provided
in response to different sleep stages in order to better regulate
sleep.
[0152] In another example, with movement disorders such as
Parkinson's disease and essential tremor, an implantable device as
described herein can be used as a sleep staging detector in
conjunction with an implantable stimulator in order to regulate the
amount of stimulation a patient receives during sleep. In some
cases, stimulation may be turned off or greatly reduced when Stage
3 or Stage 4 sleep is detected. Limiting or reducing stimulation
when it is not necessary can increase the battery life of the
stimulator.
[0153] All publications, patents, and patent applications cited
herein are hereby incorporated by reference in their entirety for
all purposes to the same extent as if each individual publication,
patent, or patent application were specifically and individually
indicated to be so incorporated by reference. Although the
foregoing implantable devices and their methods of use have been
described in some detail by way of illustration and example for
purposes of clarity of understanding, it will be readily apparent
to those of ordinary skill in the art, in light of the description
herein provided, that certain changes and modifications may be made
thereto without departing from the spirit and scope of the appended
claims.
* * * * *